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Should in-memory analysis have a seat at your big data table?
This article is part of the Business Information issue of Special Edition, September 2013
In-memory processing can serve as a high-octane fuel for supercharging big data analytics applications. But organizations should weigh factors such as additional systems infrastructure costs and the readiness of their business processes before gassing up with in-memory analytics technology. Another key step in greasing the deployment skids is identifying big data analytics problems that have proven unsolvable or that could benefit from the performance boost typically provided by in-memory analysis applications. "The integration of in-memory capabilities and big data boils down to use case and benefits," said Paul Barth, co-founder of data management and analytics consultancy NewVantage Partners. "You need to consider the business value of accelerating time to answer -- is it a matter of convenience, or is it a case when rapid turnaround and rapid analysis really benefits the decision-making process." Detecting patterns in large stockpiles of data is one application where using in-memory analytics tools makes sense, Barth said, ...
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Features in this issue
Hadoop has become everyone's big data darling. But it can only do so much, and savvy businesses need to make sure it's a good fit for their needs.
Big data and in-memory analytics software can form a mutually beneficial relationship, provided business users really need in-memory processing power.
Columns in this issue
There's real value to be gained from big data projects, if organizations can get past all the hype and look at big data tools with a realistic eye.